Lucas M. Fleuren

8.8k total citations · 1 hit paper
22 papers, 708 citations indexed

About

Lucas M. Fleuren is a scholar working on Epidemiology, Pharmacology and Surgery. According to data from OpenAlex, Lucas M. Fleuren has authored 22 papers receiving a total of 708 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Epidemiology, 10 papers in Pharmacology and 7 papers in Surgery. Recurrent topics in Lucas M. Fleuren's work include Sepsis Diagnosis and Treatment (12 papers), Antibiotics Pharmacokinetics and Efficacy (10 papers) and Hemodynamic Monitoring and Therapy (7 papers). Lucas M. Fleuren is often cited by papers focused on Sepsis Diagnosis and Treatment (12 papers), Antibiotics Pharmacokinetics and Efficacy (10 papers) and Hemodynamic Monitoring and Therapy (7 papers). Lucas M. Fleuren collaborates with scholars based in Netherlands, United States and United Kingdom. Lucas M. Fleuren's co-authors include Paul Elbers, Patrick Thoral, Armand R. J. Girbes, Luca F. Roggeveen, Tingjie Guo, Ari Ercole, Eleonora L. Swart, Mark Hoogendoorn, Charlotte Zwager and Thomas Klausch and has published in prestigious journals such as American Journal of Respiratory and Critical Care Medicine, Antimicrobial Agents and Chemotherapy and Pharmaceutical Research.

In The Last Decade

Lucas M. Fleuren

18 papers receiving 694 citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

2020 414 citations Lucas M. Fleuren, Thomas Klausch et al. Intensive Care Medicine profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Lucas M. Fleuren Netherlands	11	345	217	113	91	81	22	708
Tingjie Guo Netherlands	11	329 1.0×	190 0.9×	123 1.1×	66 0.7×	67 0.8×	24	682
Luca F. Roggeveen Netherlands	9	309 0.9×	192 0.9×	114 1.0×	72 0.8×	66 0.8×	14	612
Patrick Thoral Netherlands	15	447 1.3×	351 1.6×	86 0.8×	167 1.8×	125 1.5×	37	966
Cara O’Brien United States	14	538 1.6×	233 1.1×	29 0.3×	162 1.8×	48 0.6×	20	1.2k
Gabriel Wardi United States	17	370 1.1×	184 0.8×	21 0.2×	81 0.9×	57 0.7×	75	779
H.M. Giannini United States	4	194 0.6×	164 0.8×	34 0.3×	71 0.8×	29 0.4×	8	340
Janne Cadamuro Austria	26	150 0.4×	63 0.3×	30 0.3×	124 1.4×	83 1.0×	115	2.0k
Rabi Yacoub United States	23	233 0.7×	146 0.7×	40 0.4×	30 0.3×	106 1.3×	46	1.6k
Jens Schierbeck Denmark	10	174 0.5×	112 0.5×	32 0.3×	33 0.4×	33 0.4×	24	502
Massimo Locatelli Italy	24	123 0.4×	190 0.9×	64 0.6×	62 0.7×	397 4.9×	80	1.5k

Countries citing papers authored by Lucas M. Fleuren

Since Specialization

Citations

This map shows the geographic impact of Lucas M. Fleuren's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Lucas M. Fleuren with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lucas M. Fleuren more than expected).

Fields of papers citing papers by Lucas M. Fleuren

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lucas M. Fleuren. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Lucas M. Fleuren. The network helps show where Lucas M. Fleuren may publish in the future.

Co-authorship network of co-authors of Lucas M. Fleuren

This figure shows the co-authorship network connecting the top 25 collaborators of Lucas M. Fleuren. A scholar is included among the top collaborators of Lucas M. Fleuren based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lucas M. Fleuren. Lucas M. Fleuren is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wils, Evert‐Jan, Lucas M. Fleuren, Alexander Breskin, et al.. (2025). Early versus Delayed Switching from Controlled to Assisted Ventilation: A Target Trial Emulation. American Journal of Respiratory and Critical Care Medicine. 211(6). 975–983. 5 indexed citations

Roggeveen, Luca F., et al.. (2025). Cost-effectiveness of data driven personalised antibiotic dosing in critically ill patients with sepsis or septic shock. Journal of Clinical Monitoring and Computing. 39(5). 937–946.

Dräger, Sarah, Iris K. Minichmayr, Narges Alipanah, et al.. (2025). Dose individualisation of antibiotics in critically ill patients with inflammation: A narrative review. British Journal of Clinical Pharmacology. 91(11). 3042–3053.

Schade, Rogier P., Jurriaan E.M. de Steenwinkel, Luca F. Roggeveen, et al.. (2025). Explainable machine learning for discontinuation of therapeutic antibiotics in intensive care patients. Journal of Critical Care. 91. 155247–155247.

Fleuren, Lucas M., et al.. (2024). Racial Disparities in Pulse Oximetry, in COVID-19 and ICU Settings. Critical Care Explorations. 6(8). e1132–e1132. 1 indexed citations

Fleuren, Lucas M., Armand R. J. Girbes, Mark Hoogendoorn, et al.. (2023). Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis. Journal of Intensive Care Medicine. 38(7). 612–629. 8 indexed citations

Fleuren, Lucas M., et al.. (2023). Determining and assessing characteristics of data element names impacting the performance of annotation using Usagi. International Journal of Medical Informatics. 178. 105200–105200. 1 indexed citations

Fleuren, Lucas M., Luca F. Roggeveen, Robert F. J. Kullberg, et al.. (2023). Augmented intelligence facilitates concept mapping across different electronic health records. International Journal of Medical Informatics. 179. 105233–105233. 5 indexed citations

Roggeveen, Luca F., Tingjie Guo, Lucas M. Fleuren, et al.. (2022). Right dose, right now: bedside, real-time, data-driven, and personalised antibiotic dosing in critically ill patients with sepsis or septic shock—a two-centre randomised clinical trial. Critical Care. 26(1). 265–265. 34 indexed citations

10.

Guo, Tingjie, Alan Abdulla, Birgit C. P. Koch, et al.. (2022). Pooled Population Pharmacokinetic Analysis for Exploring Ciprofloxacin Pharmacokinetic Variability in Intensive Care Patients. Clinical Pharmacokinetics. 61(6). 869–879. 4 indexed citations

11.

Qian, Zhaozhi, William R. Zame, Lucas M. Fleuren, Paul Elbers, & Mihaela van der Schaar. (2021). Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression. Pure Amsterdam UMC. 34. 1 indexed citations

12.

Fleuren, Lucas M., Thomas Klausch, Charlotte Zwager, et al.. (2020). Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Medicine. 46(3). 383–400. 414 indexed citations breakdown →

13.

Guo, Tingjie, Reinier M. van Hest, Luca F. Roggeveen, et al.. (2020). Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients. Pharmaceutical Research. 37(9). 171–171. 13 indexed citations

14.

Fleuren, Lucas M., et al.. (2020). Machine learning in intensive care medicine: ready for take-off?. Intensive Care Medicine. 46(7). 1486–1488. 47 indexed citations

15.

Roggeveen, Luca F., Tingjie Guo, Ronald H. Driessen, et al.. (2020). Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients. Frontiers in Pharmacology. 11. 646–646. 21 indexed citations

16.

Roggeveen, Luca F., Tingjie Guo, Lucas M. Fleuren, et al.. (2020). Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis. Artificial Intelligence in Medicine. 112. 102003–102003. 23 indexed citations

17.

Fleuren, Lucas M., Luca F. Roggeveen, Tingjie Guo, et al.. (2019). Clinically relevant pharmacokinetic knowledge on antibiotic dosing among intensive care professionals is insufficient: a cross-sectional study. Critical Care. 23(1). 185–185. 14 indexed citations

18.

Zwager, Charlotte, Pieter R. Tuinman, Harm‐Jan de Grooth, et al.. (2019). Why physiology will continue to guide the choice between balanced crystalloids and normal saline: a systematic review and meta-analysis. Critical Care. 23(1). 366–366. 14 indexed citations

19.

Roggeveen, Luca F., Lucas M. Fleuren, Tingjie Guo, et al.. (2019). Right Dose Right Now: bedside data-driven personalized antibiotic dosing in severe sepsis and septic shock — rationale and design of a multicenter randomized controlled superiority trial. Trials. 20(1). 745–745. 25 indexed citations

20.

Zafar, Sahar F., Siddharth Biswal, Lucas M. Fleuren, et al.. (2017). Electronic Health Data Predict Outcomes After Aneurysmal Subarachnoid Hemorrhage. Neurocritical Care. 28(2). 184–193. 27 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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